Interpreting and Estimating the Risk of Iron ...

URL: http://www.sciencedirect.com/science/article/pii/S187770581502559X/pdf?md5=2c969452c7d1a8f92b3336dc317265bb&pid=1-s2.0-S187770581502559X-main.pdf

Metals and particulates accumulate in the distribution system and are mobilised by hydraulic events which can result in discolouration and exceedance of regulatory standards. Traditional decision tools for targeting preventive work are single parameter, based for example on proportion of unlined iron pipe or the number of customer contacts per district metering area (DMA). We show that this approach is too simplistic and propose a multivariate Decision Tree process, using the Random Under-Sampling ensemble method. The outputs gave a classification of High or Low risk per DMA. Initial findings demonstrate an 80% success rate in identifying high risk DMAs across the supply area for a UK water company.

There are no views created for this resource yet.

Additional Information

Field Value
Last updated June 16, 2016
Created June 16, 2016
Format PDF
License Creative Commons Attribution
created over 4 years ago
format PDF
id f833fe7a-8f9a-4c1c-a416-57ef6871e791
openAccess false
package id b0b32b0f-aa1a-43fe-9de8-0d6fcc872fb8
position 58
revision id f0a5a351-7af0-466a-aa97-47988e9757d6
state active
tags Decision Trees,District metering areas,Geographical Information Systems,Iron,Self-organising maps,Water quality.,Decision Trees,District metering areas,Geographical Information Systems,Iron,Self-organising maps,Water quality
topic Quality of water
type Conference Paper
year 2,015